Evaluating Machine Learning Models for Wind Energy Prediction: A Case Study Approach

The pursuit of optimizing renewable energy forecasting, specifically wind energy, has led to the employment of various machine learning (ML) and deep learning (DL) models. This article discusses a detailed evaluation of distinct ML and DL approaches utilized for wind energy prediction across three scenarios, showcasing their performance metrics and revealing insights into their effectiveness.

Case 1: Kaggle Wind Turbine SCADA Dataset Evaluation

In the first case, the evaluation utilized the Wind Turbine SCADA Dataset from Kaggle, consisting of 50,530 samples with some missing data instances. A careful split of 70% for training and 30% for testing ensured a comprehensive model evaluation. To establish benchmarks, the results were compared to findings from Karaman et al., who previously studied the same dataset.

Performance Comparison

Karaman’s analysis, depicted in Tables 3 and 4, highlighted various deep learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory networks (LSTM). LSTM emerged as the top performer, achieving an impressive R² value of 0.9694 along with low error metrics (Mean Absolute Error (MAE), Mean Squared Error (MSE)). RNN and CNN followed closely, demonstrating substantial predictive capabilities.

In contrast, the proposed machine learning models showcased further improvements. Ensemble techniques, including Random Forest, Extra Trees, and XGBoost, achieved remarkable R² values of 0.972, 0.962, and 0.980, respectively. This underlined their robustness in managing complex nonlinear interactions while maintaining minimal errors. While basic models such as Linear Regression yielded lower R² values (0.893), K-Nearest Neighbors and Decision Trees remained competitive.

Insights from the Analysis

The analysis underscores the importance of selecting advanced models to enhance the accuracy of forecasting renewable energy production. LSTM proved excellent for time-series forecasting, whereas ensemble methods like XGBoost exhibited superior accuracy, reinforcing the value of these powerful algorithms in predicting wind energy generation essential for grid stability.

Case 2: Real-Time Wind Data Analysis

The second case delved into real-time wind data sourced from Aralvaimozhi, Tamil Nadu, India, through Renewables Ninja. This dataset comprised 8,650 samples organized around attributes such as date, time, wind speed, and electricity generation.

Correlation Examination

A correlation heatmap illustrated a strong relationship (0.97) between wind speed and power generation, emphasizing that increased wind speeds correlated well with higher electricity output. Notably, time-based variables contributed minimally to power generation variance, establishing wind speed as the critical influencing factor.

Model Performance Evaluation

Performance comparisons of various models revealed distinct patterns in terms of accuracy and robustness. The Linear Regression model performed poorly, struggling to capture data complexity with a test R² of 0.935. In contrast, the LSTM model exhibited significant improvement with an R² of 0.9635 and low MAE (0.043), demonstrating its aptitude for time-series data.

The ensemble models again excelled, with the Extra Trees model achieving an exceptional R² of 0.995 and a test MAE of 0.03. Random Forest closely followed, affirming its capability in mitigating overfitting through averaging.

Enhancing Forecasting Accuracy

The Stacking Ensemble approach marked the pinnacle of this analysis, combining various advanced ensemble techniques. By integrating Random Forest, XGBoost, and LightGBM, this method provided superior forecasting accuracy and resilience. This multi-faceted approach reflects the critical role of ensemble techniques when handling complex environmental datasets, further highlighting the growing trend towards collaborative modeling in predictive analytics.

Case 3: Forecasting Wind Energy Generation for 2025

Building on insights from previous cases, the third scenario emphasized forecasting wind energy generation for 2025. Here, models such as Random Forest and XGBoost were refined to accurately capture the intricate relationships between wind speed and energy production.

Performance Assessment

Visual assessments of model performance indicated that both Random Forest and XGBoost successfully aligned predictions with actual values across training and testing datasets. Figures illustrated how Random Forest predictions closely followed actual output, while XGBoost showcased responsiveness, particularly in short-term fluctuations.

Seasonal Trends in Predictions

Seasonal forecasting visuals revealed behavioral trends across varying climatic conditions. The Random Forest model consistently exhibited stability, while XGBoost displayed more dynamic behavior during summer and monsoon months. The Stacking Ensemble model proved most proficient in generalizing across seasonal patterns, effectively maintaining accuracy and reducing variability.

Comparative Analysis

Throughout the first week of 2025, a comparative analysis revealed the strengths and weaknesses of the models. Random Forest provided reliable yet conservative estimates, while XGBoost proved sensitive to fluctuations. The Stacking Ensemble merged the benefits of both approaches, achieving balanced predictions across varying wind speeds.

Performance Metrics

Results detailed rigorous performance metrics:

• Random Forest: RMSE of 3.25 kW, MAE of 2.45 kW, R² of 0.91.
• XGBoost: RMSE of 2.98 kW, MAE of 2.21 kW, R² of 0.93.
• Stacking Ensemble: RMSE of 2.65 kW, MAE of 1.98 kW, R² of 0.95.

These statistics reaffirmed the efficacy of ensemble learning in improving wind power forecasting.

Conclusion

Overall, this comprehensive evaluation across three distinct scenarios illustrated the advancements in machine learning and deep learning techniques for wind energy prediction. The findings emphasize the critical role of sophisticated modeling approaches in enhancing forecasting accuracy, offering insights that hold significant implications for future energy management systems. By continually refining these models and incorporating additional features, the potential for optimizing wind energy generation forecasts is more promising than ever.